Introduction

Mathjax

A little math formula :

\[x = \sum_{i=1}^{n} \sqrt{\frac{y}{i}} \]

Cross-referencing

This is an example of crossreferencing figures and tables: reference to figure 3, reference to table 1.

Analysis

R output

R output is typically shown in the monospace font (here an example with the mtcars dataset in the subfolder data/):

# Import dataset from the data/ folder
mtcars <- read.csv("data/mtcars.csv")
summary(mtcars[,1:4])
##       mpg             cyl             disp             hp       
##  Min.   :10.40   Min.   :4.000   Min.   : 71.1   Min.   : 52.0  
##  1st Qu.:15.43   1st Qu.:4.000   1st Qu.:120.8   1st Qu.: 96.5  
##  Median :19.20   Median :6.000   Median :196.3   Median :123.0  
##  Mean   :20.09   Mean   :6.188   Mean   :230.7   Mean   :146.7  
##  3rd Qu.:22.80   3rd Qu.:8.000   3rd Qu.:326.0   3rd Qu.:180.0  
##  Max.   :33.90   Max.   :8.000   Max.   :472.0   Max.   :335.0

Tables

Here is a sample table output.

tab <- aggregate(. ~ Species, data = iris, mean)
kable(tab,
  caption = "Species-specific mean lengths and widths.")
Table 1: Species-specific mean lengths and widths.
Species Sepal.Length Sepal.Width Petal.Length Petal.Width
setosa 5.006 3.428 1.462 0.246
versicolor 5.936 2.770 4.260 1.326
virginica 6.588 2.974 5.552 2.026

Tabbed sections

Comparison between the classical tabs and the pill tabs:

Classical tabs

Classical tabs without fading effect.

First tab

boxplot(rnorm(100), rnorm(100))
Boxplot made with the basic graphics package

Figure 1: Boxplot made with the basic graphics package

Second tab

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Pill tabs

Pill tabs with fading effect.

First tab

plot(rnorm(100), rnorm(100))
Scatterplot made with the basic graphics package

Figure 2: Scatterplot made with the basic graphics package

Second tab

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Figures

A base graphics histogram (click on the figure to enlarge it):

hist(rnorm(100))
A Histogram made with the basic graphics package

Figure 3: A Histogram made with the basic graphics package

Interactive figures with plotly

If the package ‘plotly’ is installed, the following code block will be executed:

# If plotly is installed run:
p <- plotly::plot_ly(data = mtcars, x = ~mpg, y = ~wt, color = ~cyl)
plotly::add_markers(p)

Figure 4: This is the interactive htmlwidget ‘plotly’

Conclusions

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Bibliography

Link a .bib document via the YAML header, and the bibliography will be printed at the very end (as usual). The default bibliography style is provided in the sage-harvard.csl file (do not delete), which adopts the SAGE Harvard reference style.

References can be cited directly within the document using the R Markdown equivalent of the citation system [@key], where key is the citation key in the first line of the entry in the .bib file. Example: (Taylor and Green, 1937). To cite multiple entries, separate the keys by semicolons, e.g. (Kamm, 2000; Knupp, 1999).

There is also the package citr, which I highly recommend: citr provides functions and an RStudio add-in to search a BibTeX-file to create and insert formatted Markdown citations into the current document. If you are using the reference manager Zotero the add-in can access your reference database directly.

Software

If you want to include a paragraph on the software used, here is some example text/code to get the current R and package versions. The code to generate a bibliography file including all package references has been already added at the beginning of this script (code chunk ‘generate-package-refs’).

All analyses were performed using the statistical software R (version 4.1.2) (R Core Team, 2021). This report, including tables and figures, was generated using the packages ‘rmarkdown’ (version 2.14) (Allaire et al., 2022), and ‘knitr’ (version 1.39) (Xie, 2022).

References

Allaire J, Xie Y, McPherson J, et al. (2022) Rmarkdown: Dynamic Documents for r. Available at: https://CRAN.R-project.org/package=rmarkdown.
Kamm J (2000) Evaluation of the Sedov-von Neumann-Taylor blast wave solution. Technical Report LA-UR-00-6055. Los Alamos National Laboratory.
Knupp P (1999) Winslow smoothing on two-dimensional unstructured meshes. Eng Comput 15: 263–268.
R Core Team (2021) R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at: https://www.R-project.org/.
Taylor G and Green A (1937) Mechanism of the production of small eddies from large ones. P Roy Soc Lond A Mat 158(895): 499–521.
Xie Y (2022) Knitr: A General-Purpose Package for Dynamic Report Generation in r. Available at: https://yihui.org/knitr/.